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PURPOSE: Background phase offsets in phase-contrast MRI are often corrected using polynomial regression; however, correction performance degrades when temporally invariant outliers such as steady flow or spatial wrap-around artifact are present. We describe and validate an iterative method called automatic rejection of temporally invariant outliers (ARTO), which excludes these outliers from the fitting process. METHODS: The ARTO method iteratively removes pixels with large polynomial regression errors analyzed by a Gaussian mixture model fitting of the residual distribution. A total of 150 trials of a simulated phantom (75 with wrap-around artifact) and 125 phase-contrast MRI cines from 22 healthy subjects (48 with wrap-around artifact) were used for validation. Background phase offsets were corrected using second-order weighted regularized least squares (WRLS) with and without ARTO. Flow volumes after WRLS and WRLS+ARTO corrections were compared with the known truth (phantom) and stationary phantom reference (in vivo) using Bland-Altman analysis. The ratio between the pulmonary flow and the systemic flow was also computed in a subset of 6 subjects. RESULTS: In the simulated phantom, compared with WRLS and no correction, correction with WRLS+ARTO produced superior agreement in volumetric flow quantification with the known truth. In vivo, WRLS+ARTO also produced superior agreement with stationary phantom-corrected volumetric flow compared with WRLS and no correction. In data sets with wrap-around artifact, WRLS produced significantly larger variance in the pulmonary flow and systemic flow ratio than stationary phantom correction (P = .0008). CONCLUSION: The proposed method provides automatic exclusion of temporally invariant outliers and produces flow quantification results comparable to stationary phantom correction.
Pruitt et al. (Thu,) studied this question.